Communication system and communication control method

ABSTRACT

The communication system includes a communication unit that receives a conversation of a user, an accumulation unit that accumulates a conversation frame that describes a structure of a conversation generated on a basis of the conversation of the user collected via the communication unit, and a control unit that obtains a feeling parameter related to a feeling of the user who sends the conversation in units of the collected conversation. The control unit further extracts the conversation frame from the conversation on a basis of the feeling parameter, and accumulates the conversation frame in the accumulation unit.

CROSS-REFERENCE PARAGRAPH

The present application is a continuation application of U.S. patentapplication Ser. No. 16/068,973, filed Jul. 10, 2018, which is anational stage entry of PCT/JP2016/081986, filed Oct. 28, 2016, andclaims the benefit of priority from prior Japanese Patent Application JP2016-014238, filed Jan. 28, 2016, the entire content of which is herebyincorporated by reference. Each of the above-referenced applications ishereby incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to a communication system and acommunication control method.

BACKGROUND ART

In recent years, with the development of communication technologies,messages have frequently been exchanged via networks. Users can useinformation processing terminals such as smartphones, mobile phoneterminals, and tablet terminals to confirm messages transmitted fromother terminals and transmit messages.

In addition, an agent system that uses an information processingterminal to automatically respond to messages from a user has beenproposed. In relation to such a system, Patent Literature 1 describedbelow, for example, discloses a system that ascertains feelings that auser has on the basis of content of a conversation with the user,extracts specific keywords included in language that expresses theascertained feelings, and performs retrieval on the basis of theextracted keywords, thereby retrieving information that better coincideswith a request of the user.

In addition, Patent Literature 2 described below discloses an apparatusthat performs voice recognition on a voice response of a user inresponse to a recommendation from an agent, specifies a recognizedcharacter string, determines a type of an outline feeling on the basisof content of the recognized character string, and learns a feeling ofthe user in response to the recommendation.

CITATION LIST Patent Literature

Patent Literature 1: JP 2003-173349A

Patent Literature 2: JP 2001-117581A

DISCLOSURE OF INVENTION Technical Problem

Here, voice of an automatic response from the agent is based onconversation data generated in advance on assumption of questions andanswers with the user, and how the user feels through the conversationis not taken into consideration.

Thus, the present disclosure proposes a communication system and acommunication control method capable of leading a user to apredetermined feeling by using a conversation structure generated fromactual conversations between users.

Advantageous Effects of Invention

According to the present disclosure, there is provided a communicationsystem including: a communication unit that receives a conversation of auser; an accumulation unit that accumulates a conversation frame thatdescribes a structure of a conversation generated on a basis of theconversation of the user collected via the communication unit; and acontrol unit that obtains a feeling parameter related to a feeling ofthe user who sends the conversation in units of the collectedconversation, extracts the conversation frame from the conversation on abasis of the feeling parameter, and accumulates the conversation framein the accumulation unit.

According to the present disclosure, there is provided a communicationcontrol method including, by a processor: receiving a conversation of auser by a communication unit; accumulating, in an accumulation unit, aconversation frame that describes a structure of a conversationgenerated on a basis of the conversation of the user collected via thecommunication unit; obtaining a feeling parameter related to a feelingof the user who sends the conversation in units of the collectedconversation; and extracting the conversation frame from theconversation on a basis of the feeling parameter and accumulating theconversation frame in the accumulation unit.

According to the present disclosure, it is possible to lead a user to apredetermined feeling by using a conversation structure generated fromactual conversations between users as described above.

Note that the effects described above are not necessarily limitative.With or in the place of the above effects, there may be achieved any oneof the effects described in this specification or other effects that maybe grasped from this specification.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an explanatory diagram illustrating an overview of acommunication control system according to an embodiment of the presentdisclosure.

FIG. 2 is a diagram illustrating an overall configuration of thecommunication control system according to the embodiment.

FIG. 3 is a block diagram illustrating an example of a configuration ofa voice agent server according to the embodiment.

FIG. 4 is a diagram illustrating an example of a configuration of adialogue processing unit according to the embodiment.

FIG. 5 is a flowchart illustrating a conversation DB generation processaccording to the embodiment.

FIG. 6 is a flowchart illustrating a phoneme DB generation processaccording to the embodiment.

FIG. 7 is a flowchart illustrating a dialogue control process accordingto the embodiment.

FIG. 8 is an explanatory diagram illustrating a data configurationexample of the conversation DB according to the embodiment.

FIG. 9 is a flowchart illustrating a process of updating theconversation DB according to the embodiment.

FIG. 10 is a flowchart illustrating a conversation data transitionprocess from an individualized layer to a common layer according to theembodiment.

FIG. 11 is an explanatory diagram illustrating transition ofconversation data to a basic dialogue conversation DB according to theembodiment.

FIG. 12 is a flowchart illustrating a conversation data transitionprocess to a basic dialogue DB according to the embodiment.

FIG. 13 is a diagram illustrating an example of advertisementinformation registered in an advertisement DB according to theembodiment.

FIG. 14 is a flowchart illustrating an advertisement content insertionprocess according to the embodiment.

FIG. 15 is a diagram illustrating a configuration example of aconversation DB generation unit according to a first embodiment.

FIG. 16 is a flowchart illustrating conversation frame generationprocessing according to the first embodiment.

FIG. 17 is a flowchart illustrating happiness degree calculationprocessing according to the first embodiment.

FIG. 18 is a diagram illustrating an example of evaluation values offour factors in characteristic keywords according to the firstembodiment.

FIG. 19 is a diagram illustrating an example of conversation dataaccumulated in a conversation history DB according to the firstembodiment.

FIG. 20 is a flowchart illustrating conversation frame generationprocessing according to the first embodiment.

FIG. 21 is a diagram illustrating an example of a feeling value table ofadjectives according to the first embodiment.

FIG. 22 is a diagram illustrating an example of a conversation frameaccording to the first embodiment.

FIG. 23 is a diagram illustrating a configuration example of a dialogueprocessing unit according to the first embodiment.

FIG. 24 is a flowchart illustrating response processing according to thefirst embodiment.

FIG. 25 is a flowchart illustrating response sentence generationprocessing according to the first embodiment.

FIG. 26 is a flowchart illustrating response sentence data outputprocessing according to the first embodiment.

FIG. 27 is a diagram explaining a three-dimensional space by temperamentparameters.

FIG. 28 is a diagram illustrating a configuration example of aconversation DB generation unit according to a second embodiment.

FIG. 29 is a flowchart illustrating conversation frame generationprocessing according to the second embodiment.

FIG. 30 is a flowchart illustrating attribute analysis processingaccording to the second embodiment.

FIG. 31 is a diagram illustrating an example of three attributeparameter contribution values in characteristic keywords according tothe second embodiment.

FIG. 32 is a diagram illustrating an example of conversation dataaccumulated in a conversation history according to the secondembodiment.

FIG. 33 illustrates an example of attribute data of an uttereraccumulated in an attribute DB according to the second embodiment.

FIG. 34 is a diagram illustrating an example of a three-dimensionalspace of attribute parameter computer values and attribute typesaccording to the second embodiment.

FIG. 35 is a diagram illustrating an example of a conversation frameregistered in a conversation frame according to the second embodiment.

FIG. 36 is a diagram illustrating a configuration example of a dialogueprocessing unit according to the second embodiment.

FIG. 37 is a flowchart illustrating response sentence generationprocessing according to the second embodiment.

MODE(S) FOR CARRYING OUT THE INVENTION

Hereinafter, (a) preferred embodiment(s) of the present disclosure willbe described in detail with reference to the appended drawings. Notethat, in this specification and the appended drawings, structuralelements that have substantially the same function and structure aredenoted with the same reference numerals, and repeated explanation ofthese structural elements is omitted.

In addition, the description will be made in the following order.1. Overview of communication control system according to embodiment ofthe present disclosure

2. Configuration

2-1. System configuration2-2. Server configuration3. System operation process3-1. Conversation data registration process3-2. Phoneme DB generation process3-3. Dialogue control process3-4. Conversation DB updating process3-5. Advertisement insertion process4. Dialogue control processing according to first embodiment4-1. Generation of conversation frame(4-1-1. Configuration of conversation DB generation unit 50A)(4-1-2. Conversation frame generation processing)(4-1-3. Happiness degree calculation processing)(4-1-4. Conversation frame generation processing)4-2. Generation of response sentence(4-2-1. Configuration of dialogue processing unit 300A)(4-2-2. Response processing)(4-2-3. Response sentence generation processing)(4-2-4. Response sentence output processing)5. Dialogue control processing according to second embodiment5-1. Generation of conversation frame(5-1-1. Configuration of conversation DB generation unit 50B)(5-1-2. Conversation frame generation processing)(5-1-3. Attribute analysis processing)5-2. Generation of response sentence(5-2-1. Configuration of dialogue processing unit 300B)(5-2-2. Response sentence generation processing)

6. Conclusion

<<Overview of Communication Control System According to Embodiment ofPresent Disclosure>>

A communication control system according to an embodiment of the presentdisclosure is capable of leading a user to a predetermined feeling byusing a conversation structure generated from actual conversationsbetween users. Hereinafter, an outline of the communication controlsystem according to the embodiment will be described with reference toFIG. 1.

FIG. 1 is an explanatory diagram illustrating the overview of thecommunication control system according to an embodiment of the presentdisclosure. A voice dialogue with an agent is performed via a clientterminal 1 such as a smartphone, a mobile phone terminal, or a wearableterminal that the user owns, for example. The client terminal 1 has amicrophone and a speaker and enables dialogue between the user and theagent by collecting voice of the user and reproducing voice of theagent.

Here, on the agent side, a database in which data of questions andanswers to and from the user that are assumed to occur, for example, isaccumulated in advance is provided, and thus automatic responses can berealized by outputting response voice (response data registered inadvance) in response to spoken voice of the user.

However, such an automatic responding method does not take how the userfeels through the conversation into consideration.

Thus, the communication control system (that is, an agent system)according to the embodiment is capable of leading the user to apredetermined feeling by using a conversation structure generated fromactual conversations between users. Specifically, the communicationcontrol system first analyzes a conversation group 100 obtained bycollecting multiple actual conversation data items between the usersexchanged via a network as illustrated in FIG. 1. Regarding suchcollection of conversation data, the data is collected from messagesexchanged on social media and from voice conversations, for example.Then, the communication control system generates a conversation frame(happiness conversation frame 110) in which the user has a “happyfeeling” on the basis of a series of conversations 101 (“I made a tastystew yesterday.” “You can make a tasty stew! That's sounds great!”) inwhich the user had the predetermined feeling, for example, the “happyfeeling” (referred to as “happiness” in the specification). In thespecification, the “conversation frame” describes a conversationstructure.

Then, the communication control system fills in the correspondinghappiness conversation frame 110, for example, “You can make <adjective><noun>! That sounds great!) in response to the utterance from the user,for example, “I made a tasty gratin.” (language analysis result: “I made<adjective> <noun>.”), generates response voice 120 such as “You canmake a tasty gratin! That sounds great!” and reproduces it as a speechof the agent.

As described above, the communication control system according to theembodiment can cause the agent to output a response that gives the usera happy feeling as a speech of the agent in conversations between theuser and the agent.

In addition, the communication control system (agent system) accordingto the embodiment is not limited to a voice agent that performs aresponse by voice, and a text treatment agent that performs a responseon a text basis may be used in the client terminal 1.

2. Configuration <2-1. System Configuration>

Next, an overall configuration of the above-described communicationcontrol system according to the embodiment will be described withreference to FIG. 2. FIG. 2 is a diagram illustrating an overallconfiguration of the communication control system according to theembodiment.

As illustrated in FIG. 2, the communication control system according tothe embodiment includes the client terminal 1 and an agent server 2.

The agent server 2 is connected to the client terminal 1 via a network 3and transmits and receives data. Specifically, the agent server 2generates response voice to spoken voice collected and transmitted bythe client terminal 1 and transmits the response voice to the clientterminal 1. The agent server 2 includes a phoneme database (DB)corresponding to one or more agents and can generate response voicethrough the voice of a specific agent. Herein, the agent may be acharacter of a cartoon, an animation, a game, a drama, or a movie, anentertainer, a celebrity, a historical person, or the like or may be,for example, an average person of each generation without specifying anindividual. In addition, the agent may be an animal or a personifiedcharacter. In addition, the agent may be a person in whom thepersonality of the user is reflected or a person in whom the personalityof a friend, a family member, or an acquaintance of the user isreflected.

In addition, the agent server 2 can generate response content in whichthe personality of each agent is reflected. The agent server 2 cansupply various services such as management of a schedule of the user,transmission and reception of messages, and supply of informationthrough dialogue with the user via the agent.

The client terminal 1 is not limited to the smartphone illustrated inFIG. 2. For example, a mobile phone terminal, a tablet terminal, apersonal computer (PC), a game device, a wearable terminal (smarteyeglasses, a smart band, a smart watch, or a smart necklace) may alsobe used. In addition, the client terminal 1 may also be a robot.

The overview of the communication control system according to theembodiment has been described above. Next, a configuration of the agentserver 2 of the communication control system according to the embodimentwill be described specifically with reference to FIG. 3.

<2-2. Agent Server 2>

FIG. 3 is a block diagram illustrating an example of the configurationof the agent server 2 according to the embodiment. As illustrated inFIG. 3, the agent server 2 includes a voice agent interface (I/F) 20, adialogue processing unit 30, a phoneme storage unit 40, a conversationDB generation unit 50, a phoneme DB generation unit 60, an advertisementinsertion processing unit 70, an advertisement DB 72, and a feedbackacquisition processing unit 80.

The voice agent I/F 20 functions as an input and output unit, a voicerecognition unit, and a voice generation unit for voice data. As theinput and output unit, a communication unit that transmits and receivesdata to and from the client terminal 1 via the network 3 is assumed. Thevoice agent I/F 20 can receive the spoken voice of the user from theclient terminal 1, process the voice, and convert the spoken voice intotext through voice recognition. In addition, the voice agent I/F 20processes answer sentence data (text) of the agent output from thedialogue processing unit 30 to vocalize answer voice using phoneme datacorresponding to the agent and transmits the generated answer voice ofthe agent to the client terminal 1.

The dialogue processing unit 30 functions as an arithmetic processingdevice and a control device and controls overall operations in the agentserver 2 in accordance with various programs. The dialogue processingunit 30 is realized by, for example, an electronic circuit such as acentral processing unit (CPU) or a microprocessor. In addition, thedialogue processing unit 30 according to the embodiment functions as abasic dialogue processing unit 31, a character A dialogue processingunit 32, a person B dialogue processing unit 33, and a person C dialogueprocessing unit 34.

The character A dialogue processing unit 32, the person B dialogueprocessing unit 33, and the person C dialogue processing unit 34 realizedialogue specialized for each agent. Herein, examples of the agentinclude a “character A,” a “person B,” and a “person C” and theembodiment is, of course, not limited thereto. Each dialogue processingunit realizing dialogue specialized for many agents may be furtherincluded. The basic dialogue processing unit 31 realizes general-purposedialogue not specialized for each agent.

Herein, a basic configuration common to the basic dialogue processingunit 31, the character A dialogue processing unit 32, the person Bdialogue processing unit 33, and the person C dialogue processing unit34 will be described with reference to FIG. 4.

FIG. 4 is a diagram illustrating an example of a configuration of thedialogue processing unit 300 according to the embodiment. As illustratedin FIG. 4, the dialogue processing unit 300 includes a question sentenceretrieval unit 310, an answer sentence generation unit 320, a phonemedata acquisition unit 340, and a conversation DB 330. The conversationDB 330 stores CONVERSATION data in which question sentence data andanswer sentence data are paired. In the dialogue processing unitspecialized for the agent, conversation data specialized for the agentis stored in the conversation DB 330. In a general-purpose dialogueprocessing unit, general-purpose data (that is, basic conversation data)not specific to the agent is stored in the conversation DB 330.

The question sentence retrieval unit 310 recognizes question voice(which is an example of spoken voice) of the user output from the voiceagent I/F 20 and retrieves question sentence data matching the questionsentence converted into text from the conversation DB 330. The answersentence generation unit 320 extracts the answer sentence data stored inassociation with the question sentence data retrieved by the questionsentence retrieval unit 310 from the conversation DB 330 and generatesthe answer sentence data. The phoneme data acquisition unit 340 acquiresphoneme data for vocalizing an answer sentence generated by the answersentence generation unit 320 from the phoneme storage unit 40 of thecorresponding agent. For example, in the case of the character Adialogue processing unit 32, phoneme data for reproducing answersentence data through the voice of the character A is acquired from thecharacter A phoneme DB 42. Then, the dialogue processing unit 300outputs the generated answer sentence data and the acquired phoneme datato the voice agent I/F 20.

The phoneme storage unit 40 stores a phoneme database for generatingvoice of each agent. The phoneme storage unit 40 can be realized by aread-only memory (ROM) and a random access memory (RAM). In the exampleillustrated in FIG. 3, a basic phoneme DB 41, a character A phoneme DB42, a person B phoneme DB 43, and a person C phoneme DB 44 are stored.In each phoneme DB, for example, a phoneme segment and a prosodic modelwhich is control information for the phoneme segment are stored asphoneme data.

The conversation DB generation unit 50 has a function of generating theconversation DB 330 of the dialogue processing unit 300. For example,the conversation DB generation unit 50 collects assumed questionsentence data, collects answer sentence data corresponding to eachquestion, and subsequently pairs and stores the question sentence dataand the answer sentence data. Then, when a predetermined number ofpieces of conversation data (pairs of question sentence data and answersentence data: for example, 100 pairs) are collected, the conversationDB generation unit 50 registers the conversation data as a set ofconversation data of the agent in the conversation DB 330.

The phoneme DB generation unit 60 has a function of generating thephoneme DB stored in the phoneme storage unit 40. For example, thephoneme DB generation unit 60 analyzes voice information ofpredetermined read text, decomposes the voice information into thephoneme segment and the prosodic model which is control information, andperforms a process of registering a predetermined number or more ofpieces of voice information as phoneme data in the phoneme DB when thepredetermined number or more of pieces of voice information arecollected.

The advertisement insertion processing unit 70 has a function ofinserting advertisement information into dialogue of the agent. Theadvertisement information to be inserted can be extracted from theadvertisement DB 72. In the advertisement DB 72, advertisementinformation (for example, information such as advertisement content oftext, an image, voice, or the like, an advertiser, an advertisementperiod, and an advertisement target person) requested by a supply sidesuch as a company (a vendor or a supplier) is registered.

The feedback acquisition processing unit 80 has a function of insertinga question for acquiring feedback into dialogue of the agent andobtaining the feedback from the user.

The configuration of the agent server 2 according to the embodiment hasbeen described specifically above. Note that the configuration of theagent server 2 according to the embodiment is not limited to the exampleillustrated in FIG. 3. For example, each configuration of the agentserver 2 may be achieved by another server on a network.

Next, a basic operation process of the communication control systemaccording to the embodiment will be described with reference to FIGS. 5to 14.

3. System Operation Process <3-1. Conversation Data RegistrationProcess>

FIG. 5 is a flowchart illustrating a generation process of theconversation DB 330 according to the embodiment. As illustrated in FIG.5, the conversation DB generation unit 50 first stores assumed questionsentences (step S103).

Subsequently, the conversation DB generation unit 50 stores answersentences corresponding to (paired with) the question sentences (stepS106).

Subsequently, the conversation DB generation unit 50 determines whethera predetermined number of pairs of question sentences and answersentences (also referred to as conversation data) are collected (stepS109).

Then, in a case in which the predetermined number of pairs of questionsentences and conversation sentences are collected (Yes in step S109),the conversation DB generation unit 50 registers the data sets formed ofmany pairs of question sentences and answer sentences in theconversation DB 330 (step S112). As examples of the pairs of questionsentences and answer sentences, for example, the following pairs areassumed.

Examples of pairs of question sentences and answer sentences

Pair 1

Question sentence: Good morning.Answer sentence: How are you doing today?

Pair 2

Question sentence: How's the weather today?Answer sentence: Today's weather is 00.The pairs can be registered as conversation data in the conversation DB330.

<3-2. Phoneme DB Generation Process>

FIG. 6 is a flowchart illustrating a phoneme DB generation processaccording to the embodiment. As illustrated in FIG. 6, the phoneme DBgeneration unit 60 first displays an example sentence (step S113). Inthe display of the example sentence, for example, an example sentencenecessary to generate phoneme data is displayed on a display of aninformation processing terminal (not illustrated).

Subsequently, the phoneme DB generation unit 60 records voice readingthe example sentence (step S116) and analyzes the recorded voice (stepS119). For example, voice information read by a person who takes chargeof the voice of an agent is collected by the microphone of theinformation processing terminal. Then, the phoneme DB generation unit 60receives and stores the voice information and further performs voiceanalysis.

Subsequently, the phoneme DB generation unit 60 generates a prosodicmodel on the basis of the voice information (step S122). The prosodicmodel extracts prosodic parameters indicating prosodic features of thevoice (for example, a tone of the voice, strength of the voice, and aspeech speed) and differs for each person.

Subsequently, the phoneme DB generation unit 60 generates a phonemesegment (phoneme data) on the basis of the voice information (stepS125).

Subsequently, the phoneme DB generation unit 60 stores the prosodicmodel and the phoneme segment (step S128).

Subsequently, the phoneme DB generation unit 60 determines whether apredetermined number of the prosodic models and the phoneme segments arecollected (step S131).

Then, in a case in which the predetermined number of prosodic models andphoneme segments are collected (Yes in step S131), the phoneme DBgeneration unit 60 registers the prosodic models and the phonemesegments as a phoneme database for a predetermined agent in the phonemestorage unit 40 (step S134).

<3-3. Dialogue Control Process>

FIG. 7 is a flowchart illustrating a dialogue control process accordingto the embodiment. As illustrated in FIG. 7, the voice agent I/F 20first confirms whether question voice and an agent ID of a user areacquired (step S143). The agent ID is identification informationindicating a specific agent such as the character A, the person B, orthe person C. The user can purchase phoneme data of each agent. Forexample, an ID of the agent purchased in a purchase process is stored inthe client terminal 1.

Subsequently, when the question voice and the agent ID of the user areacquired (Yes in step S146), the voice agent I/F 20 converts thequestion voice into text through voice recognition (step S149). Thevoice agent I/F 20 outputs the question sentence converted into text tothe dialogue processing unit of the specific agent designated with theagent ID. For example, in the case of “agent ID: agent A” the voiceagent I/F 20 outputs the question sentence converted into text to thecharacter A dialogue processing unit 32.

Subsequently, the dialogue processing unit 30 retrieves a questionsentence matching the question sentence converted into text from theconversation DB of the specific agent designated with the agent ID (stepS152).

Subsequently, in a case in which there is a matching question (Yes instep S155), the character A dialogue processing unit 32 acquires answersentence data corresponding to (paired with and stored) the questionfrom the conversation DB of the specific agent (step S158).

Conversely, in a case in which there is no matching question (No in stepS155), a question sentence matching the question sentence converted intotext is retrieved from the conversation DB of the basic dialogueprocessing unit 31 (step S161).

In a case in which there is a matching question sentence (Yes in stepS161), the basic dialogue processing unit 31 acquires the answersentence data corresponding to (paired with and stored) the questionfrom the conversation DB of the basic dialogue processing unit 31 (stepS167).

Conversely, in a case in which there is no matching question (No in stepS164), the basic dialogue processing unit 31 acquires answer sentencedata (for example, an answer sentence “I don't understand the question”)in a case in which there is no matching question sentence (step S170).

Subsequently, the character A dialogue processing unit 32 acquiresphoneme data of the character A for generating voice of the answersentence data with reference to the phoneme DB (herein, the character Aphoneme DB 42) of the specific agent designated with the agent ID (stepS173).

Subsequently, the acquired phoneme data and answer sentence data areoutput to the voice agent I/F 20 (step S176).

Then, the voice agent I/F 20 vocalizes the answer sentence data (text)(voice synthesis) using the phoneme data and transmits the answersentence data to the client terminal 1 (step S179). The client terminal1 reproduces the answer sentence through the voice of the character A.

<3-4. Conversation DB Updating Process>

Next, a process of updating the conversation DB 330 of each dialogueprocessing unit 300 will be described. In the embodiment, it is possibleto extend the conversation DB 330 by a conversation with a user.

First, a data configuration example of the conversation DB 330 will bedescribed supplementarily with reference to FIG. 8. FIG. 8 is anexplanatory diagram illustrating a data configuration example of theconversation DB 330 according to the embodiment. As illustrated in FIG.8, each conversation DB 330 includes two layers, an individualized layer331 and a common layer 332. For example, in the case of a character Aconversation DB 330A, conversation data in which personality or afeature of the character A is reflected is retained in the common layer332A. On the other hand, in an individualized layer 331A, conversationdata customized only for a user through a conversation with the user isretained. That is, the character A phoneme DB 42 and the character Adialogue processing unit 32 are supplied (sold) as a set to users. Then,certain users X and Y perform dialogues with the same character A atfirst (conversation data retained in the common layer 332A is used).However, as the dialogues continue, conversation data customized onlyfor each user is accumulated in the individualized layer 331A for eachuser. In this way, it is possible to supply the users X and Y withdialogues with the character A in accordance with preferences of theusers X and Y.

In addition, even in a case in which the agent “person B” is an averageperson of each generation who has no specific personality such as thecharacter A, the conversation data can be customized only for the user.That is, for example, in a case in which the “person B” is a “person inhis or her twenties,” average conversation data of his or her twentiesis retained in the common layer 332B and dialogue with the user iscontinued so that the customized conversation data is retained in theindividualized layer 331B of each user. As dialogues with the usercontinue, customized conversation data is retained in the individualizedlayer 331B for each user. In addition, the user can also select favoritephoneme data such as “male,” “female,” “high-tone voice,” or “low-tonevoice” as the voice of the person B from the person B phoneme DB 43 andcan purchase the favorite phoneme data.

A specific process at the time of the customization of the conversationDB 330 will be described with reference to FIG. 9. FIG. 9 is a flowchartillustrating a process of updating the conversation DB 330 according tothe embodiment.

As illustrated in FIG. 9, the voice agent I/F 20 first acquires(receives) question voice of the user from the client terminal 1 andconverts the question voice into text through voice recognition (stepS183). The data (question sentence data) converted into text is outputto the dialogue processing unit (herein, for example, the character Adialogue processing unit 32) of the specific agent designated by theagent ID.

Subsequently, the character A dialogue processing unit 32 determineswhether the question sentence data is a predetermined command (stepS186).

Subsequently, in a case in which the question sentence data is thepredetermined command (Yes in step S186), the character A dialogueprocessing unit 32 registers answer sentence data designated by the useras a pair with the question sentence data in the individualized layer331A of the conversation DB 330A (step S189). The predetermined commandmay be, for example, a word “NG” or “Setting.” For example, theconversation DB of the character A can be customized in accordance witha flow of the following conversation.

User: “Good morning”Character A: “Good morning”User: “NG. Answer to fine do your best”Character A: “Fine do your best”

In the flow of the foregoing conversation, “NG” is the predeterminedcommand. After “NG” is spoken by the user, the character A dialogueprocessing unit 32 registers answer sentence data “Fine do your best”designated by the user as a pair with the question sentence data “Goodmorning” in the individualized layer 331A of the conversation DB 330A.

Conversely, in a case in which the question sentence data is not thepredetermined command (No in step S186), the character A dialogueprocessing unit 32 retrieves the answer sentence data retained as thepair with the question sentence data from the character A conversationDB 330A. In a case in which the answer sentence data retained as thepair with the question sentence data is not retained in the character Aconversation DB 330A, that is, a question of the user is a question withno answer sentence (Yes in step S192), the character A dialogueprocessing unit 32 registers the answer sentence data designated by theuser as a pair with the question sentence in the individualized layer331A (step S195). For example, in a flow of the following conversation,the conversation DB of the character A can be customized.

User A: “Fine?”

Character A: “I can't understand the question” (answer data example incase in which there is no corresponding answer)User: “When I questions “Fine?,” answer to “Fine today””Character A: “Fine today”

In the flow of the foregoing conversation, since there is no answersentence data maintained to be paired with “Fine?,” “I can't understandthe question” which is an example of the answer data in the case inwhich there is no corresponding answer is acquired by the character Adialogue processing unit 32, is output along with corresponding phonemedata of the character A to the voice agent I/F 20, and is reproduced inthe client terminal 1. Subsequently, when the answer sentence “Finetoday” designated by the user is input, the character A dialogueprocessing unit 32 registers “Fine today” as the pair with the questionsentence data “Fine?” in the individualized layer 331A.

Conversely, in a case in which the question of the user is a questionfor which there is an answer sentence (No in step S192), the character Adialogue processing unit 32 acquires the answer sentence data andoutputs the answer sentence data along with the corresponding phonemedata of the character A to the voice agent I/F 20. Then, the answersentence is reproduced through the voice of the character A in theclient terminal 1 (step S198).

Next, conversation data transition from an individualized layer to acommon layer will be described with reference to FIG. 10. FIG. 10 is aflowchart illustrating conversation data transition process from anindividualized layer to a common layer according to the embodiment.Herein, for example, the conversation data transition process from theindividualized layer 331A to the common layer 332A of the character Adialogue processing unit 32 will be described.

As illustrated in FIG. 10, the character A dialogue processing unit 32first searches the individualized layer 331A for each user periodically(step S203) and extracts conversation pairs with substantially the samecontent (the pair of question sentence data and answer sentence data)(step S206). For the conversation pairs with the substantially samecontent, for example, a pair of question sentence “Fine?” and answersentence “Fine today!” and a pair of question sentence “Are you fine?”and answer sentence “Fine today!” can be determined to be theconversation pairs with substantially the same content because thequestion sentences are different only in a polite expression or not.

Subsequently, when a predetermined number or more of conversation pairsare extracted from the individualized layer 331A for each user (Yes instep S209), the character A dialogue processing unit 32 registers theconversation pairs in the common layer 332A (for each user) (step S212).

In this way, when the conversation pairs with substantially the samecontent in the individualized layer 331 for each user transition to thecommon layer 332, the common layer 332 can be extended (the conversationpairs can be expanded).

In addition, in the embodiment, the conversation data can transitionfrom the conversation DB (specifically, the common layer) of thespecific agent to the basic dialogue conversation DB, and thus the basicdialogue conversation DB can also be extended. FIG. 11 is an explanatorydiagram illustrating transition of conversation data to the basicdialogue conversation DB 330F according to the embodiment. For example,in a case in which the users X and Y each select (purchase) the agent“character A” and a user Z selects (purchases) the agent “person B,” asillustrated in FIG. 11, a character A conversation DB 330A-X of the userX, a character A conversation DB 330A-Y of the user Y, and a person Bconversation DB 330-Z of the user Z can be in the dialogue processingunit 30. In this case, in individualized layers 331A-X, 331A-Y, and331B-Z, unique (customized) conversation pairs are gradually registeredin accordance with dialogues with the users X, Y, and Z (see FIG. 9).Subsequently, when substantially the same conversation pairs in the sameindividualized layers 331A-X and 331A-Y become a predetermined number,substantially the same conversation pairs are registered in commonlayers 332A-X, 332A-Y for the users, respectively (see FIG. 10).

Then, in a case in which a predetermined number or more of substantiallysame conversation pairs are extracted from the common layers 332A-X,332A-Y, and 332B-Z of the plurality of agents (which may includedifferent agents), the dialogue processing unit 30 causes theconversation pairs to transition to a high-order basic dialogueconversation DB 330F. The basic dialogue conversation DB 330F is aconversation DB included in the basic dialogue processing unit 31. Thus,it is possible to extend the basic dialogue conversation DB 330F (expandthe conversation pairs). The data transition process will be describedspecifically with reference to FIG. 12. FIG. 12 is a flowchartillustrating the conversation data transition process to the basicdialogue DB 330F according to the embodiment.

As illustrated in FIG. 12, the dialogue processing unit 30 firstsearches the plurality of common layers 332 of the conversation DBs 330periodically (step S223) and extracts substantially the sameconversation pairs (step S226).

Subsequently, when the predetermined number or more of substantiallysame conversation pairs are extracted from the plurality of commonlayers 332 (Yes in step S229), the dialogue processing unit 30 registersthe conversation pairs in the basic dialogue conversation DB 330F (stepS232).

In this way, by causing the conversation pairs with substantially thesame content in the common layers 332 of the conversation DBs 330 in theplurality of agents to transition to the basic dialogue conversation DB330F, it is possible to extend the basic dialogue conversation DB 330F(expand the conversation pairs).

<3-5. Advertisement Output Process>

Next, an advertisement information insertion process by theadvertisement insertion processing unit 70 will be described withreference to FIGS. 13 and 14. In the embodiment, the advertisementinsertion processing unit 70 can insert advertisement information storedin the advertisement DB 72 into speech of an agent. The advertisementinformation can be registered in advance in the advertisement DB 72.FIG. 13 is a diagram illustrating an example of advertisementinformation registered in the advertisement DB 72 according to theembodiment.

As illustrated in FIG. 13, advertisement information 621 includes, forexample, an agent ID, a question sentence, advertisement content, acondition, and a probability. The agent ID designates an agent speakingadvertisement content, the question sentence designates a questionsentence of a user which serves as a trigger and into whichadvertisement content is inserted, and the advertisement content is anadvertisement sentence inserted into dialogue of an agent. In addition,the condition is a condition on which advertisement content is insertedand the probability indicates a probability at which advertisementcontent is inserted. For example, in an example illustrated in the firstrow of FIG. 13, in a case in which a word “chocolate” is included in aquestion sentence from a user who is 30 years old or less in dialoguewith the agent “character A,” advertisement content “chocolate newlyreleased by “BB company is delicious because milk is contained much” isinserted into the question sentence. In addition, when the advertisementcontent is inserted every time at the time of speaking the questionsentence serving as a trigger, the user feels troublesome. Therefore, inthe embodiment, a probability at which the advertisement is inserted maybe set. The probability may be decided in accordance with advertisementcharges. For example, the probability is set to be higher as theadvertisement charges are higher.

The advertisement content insertion process will be describedspecifically with reference to FIG. 14. FIG. 14 is a flowchartillustrating the advertisement content insertion process according tothe embodiment.

As illustrated in FIG. 14, the advertisement insertion processing unit70 first monitors dialogue (specifically, a dialogue process by thedialogue processing unit 30) between the user and the agent (step S243).

Subsequently, the advertisement insertion processing unit 70 determineswhether a question sentence with the same content as a question sentenceregistered in the advertisement DB 72 appears in the dialogue betweenthe user and the agent (step S246).

Subsequently, in a case in which the question sentence with the samecontent appears (Yes in step S246), the advertisement insertionprocessing unit 70 confirms the condition and the probability of theadvertisement insertion associated with the corresponding questionsentence (step S249).

Subsequently, the advertisement insertion processing unit 70 determineswhether a current state is an advertising state on the basis of thecondition and the probability (step S252).

Subsequently, in a case in which the current state is the advertisingstate (Yes in step S252), the advertisement insertion processing unit 70temporarily interrupts the dialogue process by the dialogue processingunit 30 (step S255) and inserts the advertisement content into thedialogue (step S258). Specifically, for example, the advertisementcontent is inserted into an answer sentence of the agent for thequestion sentence of the user.

Then, the dialogue (conversation sentence data) including theadvertisement content is output from the dialogue processing unit 30 tothe voice agent I/F 20, is transmitted from the voice agent I/F 20 tothe client terminal 1, and is reproduced through voice of the agent(step S261). Specifically, for example, the advertisement content can bepresented as a speech of the character A to the user, for example, inthe following conversation.

User: “Good morning”Character A: “Good morning! How are you doing today?”User: “Fine. I want to eat some delicious food”

Character A: “I heard that grilled meat at CC store is delicious”

In the conversation, the corresponding answer sentence “Good morning!How are you doing today?” retrieved from the conversation DB of thecharacter A is first output as voice in response to the questionsentence “Good Morning” of the user. Subsequently, since the questionsentence “I want to eat some delicious food” serving as the trigger ofthe advertisement insertion is included in the question sentence “Fine.I want to eat some delicious food” of the user (see second row of FIG.13), the advertisement insertion processing unit 70 performs theadvertisement insertion process and outputs the answer sentence with theadvertisement content “I heard that grilled meat at CC store isdelicious” through the voice of the character A.

The conversation data registration process, the phoneme DB generationprocess, the dialogue control process, the conversation DB updatingprocess, and the advertisement insertion process have been describedabove as the basic operation processes of the communication controlsystem according to the embodiment.

Note that the dialogue control processing according to the embodiment isnot limited to the aforementioned example. The dialogue processing unit30 can generate a response from the agent by using a conversation framethat leads the user to a predetermined feeling. Hereinafter, specificdescription will be given with reference to FIGS. 15 to 37.

4. Dialogue Control Processing According to First Embodiment <4-1.Generation of Conversation Frame>

First, generation of a conversation frame will be described. Thegeneration of the conversation frame can be performed by a conversationDB generation unit 50. As described above, the conversation DBgeneration unit 50 according to the embodiment saves conversationsentence data as a pair of question sentence data and answer sentencedata assumed in advance. If a predetermined amount (for example, 100pairs) of the conversation sentence data is collected, the conversationDB generation unit 50 outputs the conversation sentence data as aconversation sentence data set to the dialogue processing unit 30, andthe conversation sentence data set is stored in a predeterminedconversation DB 330 (see FIG. 4). The generation processing performed bythe conversation DB generation unit 50 according to the embodiment isnot limited thereto, and it is also possible to generate a conversationframe. Hereinafter, a main configuration of a conversation DB generationunit 50A that generates a conversation frame will be described withreference to FIG. 15.

(4-1-1. Configuration of Conversation DB Generation Unit 50A)

FIG. 15 is a diagram illustrating a configuration example of theconversation DB generation unit 50A according to the first embodiment.As illustrated in FIG. 15, the conversation DB generation unit 50A has acontrol unit 500, a communication unit 510, a conversation history DB511, and a conversation frame DB 512.

The communication unit 510 is connected to an external device in a wiredor wireless manner and has a function of transmitting and receivingdata. For example, the communication unit 510 is connected to theInternet and collects messages exchanged between users and voiceconversations from a variety of social media servers and mail servers onthe network.

The conversation history DB 511 stores the conversations between theusers collected by the communication unit 510. Note that in a case ofvoice conversations, data obtained by converting the voice conversationinto texts by voice analysis may also be accumulated together. The voiceanalysis of the voice conversations is performed by the control unit500, for example. In addition, voice conversations between the user andthe agent performed via a voice agent I/F 20 and conversation dataobtained by changing voice conversations between users into texts mayalso be accumulated in the conversation history DB 511. Content of theconversation data, dates and times of conversations, IDs of persons whohave made conversations, and parameters such as happiness degrees, whichwill be described later, are linked to each other and stored in theconversation history DB 511.

The control unit 500 functions as an arithmetic processing device and acontrol device and controls overall operations in the conversation DBgeneration unit 50A in accordance with a variety of programs. Thecontrol unit 500 is realized by an electronic circuit such as a CPU or amicroprocessor, for example. In addition, the control unit 500 accordingto the embodiment functions as a happiness calculation unit 501 and aconversation frame generation unit 502.

The happiness calculation unit 501 calculates how happy an utterer ofeach conversation feels on the basis of the conversation dataaccumulated in the conversation history DB 511, links the calculatedvalue as a degree of happiness to the conversation data, and saves thecalculated value in the conversation history DB 511. The degree ofhappiness can be calculated on the basis of characteristic keywordsincluded in the conversation, for example. A detailed method ofcalculating the degree of happiness will be described later.

The conversation frame generation unit 502 generates a conversationframe that describes a structure of a conversation for leading the userto a predetermined feeling (here, a “feeling of happiness” as anexample). For example, the conversation frame generation unit 502 refersto the conversation data accumulated in the conversation history DB 511,extracts a conversation through which the degree of happiness of theutterer increases, generates a conversation frame on the basis of theconversation exchanged chronologically before the increase in the degreeof happiness, and accumulates the conversation frame in the conversationframe DB 512. A detailed method of generating the conversation framewill be described later.

The conversation frame generated by the conversation frame generationunit 502 is accumulated in the conversation frame DB 512.

The control unit 500 outputs the conversation frame accumulated in theconversation frame DB 512 to the dialogue processing unit 30. Theconversation frame is used when the dialogue processing unit 30generates a response sentence from the agent.

The specific configuration of the conversation DB generation unit 50Aaccording to the first embodiment has been described above. Next,operation processing performed by the conversation DB generation unit50A according to the first embodiment will be described.

(4-1-2. Conversation Frame Generation Processing)

FIG. 16 is a flowchart illustrating conversation frame generationprocessing according to the first embodiment. As illustrated in FIG. 16,the conversation DB generation unit 50A first acquires conversation databetween users from social media on the network, for example, via thecommunication unit 510 (Step S303). The acquired conversation data isaccumulated in the conversation history DB 511.

Next, the happiness calculation unit 501 calculates a degree ofhappiness of the utterer of each conversation on the basis of theconversation data accumulated in the conversation history DB 511 (StepS306). Although various definitions of the degree of happiness can beconsidered, “factors for being happy” defined in “Mechanism ofHappiness” (Kodansha Gendai Shinsho) written by Takashi Maeno, aprofessor of a graduate school of Keio University are used as examplesin this embodiment. Here, the following four factors are listed as the“factors for being happy”.

-   -   “Self-fulfillment and growth” factor regarding self-growth and        features directed to a person himself/herself in order to        achieve a goal    -   “Connection and thankfulness” factor regarding a thankfulness        tendency and features directed to other persons    -   “Forward-thinking and optimism” factor indicating that a person        is optimistic, positive, and mentally stable    -   “Independence and own pace” factor indicating that a person has        defined himself/herself and is characterized by not comparing        himself/herself with other persons

In this embodiment, larger values of these respective four factors areestimated to indicate that the utterer feels happier. Therefore, adegree of happiness (how happy the utterer feels) can be calculated onthe basis of a frequency at which characteristic keywords correspondingto the respective factors are included in conversation data, forexample. An example of the characteristic keywords corresponding to therespective factors will be described later with reference to FIG. 18.

The calculated degree of happiness is linked to each conversation and isstored in the conversation history DB 511.

Then, the conversation frame generation unit 502 refers to the degree ofhappiness of each conversation and generates a conversation frame bywhich the user has a happy feeling (Step S309). The generatedconversation frame is accumulated in the conversation frame DB 512.

Then, Steps S303 to S309 described above are repeated until aninstruction for ending them is provided (Step S312). Steps S303 to S309described above are periodically (such as once a day or once in sixhours) performed, for example.

(4-1-3. Happiness Degree Calculation Processing)

Next, the happiness degree calculation processing performed in Step S306described above will be described in detail. FIG. 17 is a flowchartillustrating the happiness degree calculation processing according tothe first embodiment.

As illustrated in FIG. 17, the happiness calculation unit 501 firstperforms language analysis on the conversation data accumulated in theconversation history DB 511 (Step S323). For example, the happinesscalculation unit 501 performs morpheme analysis on the conversation data(texts).

Then, the happiness calculation unit 501 performs expression retrievalon the conversation data on the basis of the analysis result (Step S326)and calculates a degree of happiness (Step S329). Here, the “factors ofhappiness” based on analysis of psychological factors of subjectivehappiness are used when the degree of happiness indicating how happy theutterer feels is calculated in this embodiment as described above.Specifically, the degree of happiness is calculated on the basis of afrequency at which characteristic keywords corresponding to the fourfactors, which are the “factors of happiness”, are included in theconversation data. Here, an example of evaluation values of the fourfactors in the characteristic keywords will be shown in FIG. 18. Thehappiness calculation unit 501, for example, has the table illustratedin FIG. 18.

The four factors illustrated in FIG. 18 are Factor 1: a self-fulfillmentand growth factor, Factor 2: a connection and thankfulness factor,Factor 3: a forward-thinking and optimism factor, and Factor 4: anindependence and own pace factor. In addition, the evaluation values(that is, happiness degree contribution values) of the four factors areexpressed as 0 to 1. In a case in which a keyword (that is, aconversation expression) “self-fulfillment” in the first line isincluded in conversation data, for example, the evaluation value of theself-fulfillment and growth factor (Factor 1) is “1” while theevaluation values of the other factors are “0”. In addition, in a casein which a keyword of “Thank you” in the fourth line is included inconversation data, the evaluation value of the connection andthankfulness factor (Factor 2) is “1” while the evaluation values of theother factors are “0”.

Therefore, the happiness calculation unit 501 retrieves all expressions(keywords) included in the table illustrated in FIG. 18 from theconversation data on the basis of the result of the morpheme analysis(word extraction) of the conversation data and obtains a vector of adegree of happiness obtained by adding the contribution values of therespective factors for each discovered expression. If it is assumed thatthe contribution value of each factor is f_(1 to 4), the vector of thedegree of happiness is obtained by the following Equation 1.

[Math. 1]

happiness=[Σ_(i) f _(1i),Σ_(i) f _(2i),Σ_(i) f _(3i),Σ_(i) f _(4i)]  Equation 1

For example, the expression “Thank you” in the table for the happinessdegree contribution values is included in conversation data “Not reallygreat, but thank you”, and the happiness degree contribution value ishappiness=[0, 1, 0, 0] (the value of Factor 2 is “1”) referring to thetable illustrated in FIG. 18.

Then, the happiness calculation unit 501 saves the calculated degree ofhappiness as metadata of conversation data in the conversation historyDB 511 (Step S332). Here, FIG. 19 illustrates an example of conversationdata accumulated in the conversation history DB 511. The conversationdata is data to which conversation IDs, dates and times ofconversations, IDs of persons who have made conversation, texts (contentof conversations), and degrees of happiness are linked. Since theconversation ID: C02-03 illustrated in FIG. 19 includes a characteristickeyword “plan” in content of the conversation, the value of Factor 1 isdetermined to be “0.5” referring to the table illustrated in FIG. 18,and the happiness degree contribution value is evaluated ashappiness=[0.5, 0, 0, 0].

Then, Steps S323 to S332 described above are repeated until aninstruction for ending them is provided (Step S335). Steps S323 to S332described above are periodically (such as once a day or once in sixhours) performed, for example.

(4-1-4. Conversation Frame Generation Processing)

Next, the conversation frame generation processing performed in StepS309 described above will be described in detail. FIG. 20 is a flowchartillustrating the conversation frame generation processing according tothe first embodiment.

As illustrated in FIG. 20, the conversation frame generation unit 502first extracts conversation data that has become a factor of an increasein the degree of happiness from the conversation history DB 511 (StepS343). Specifically, in a case in which a degree of happiness ofconversation data of the same utterer ID has increased in a series ofconversations in the conversation data accumulated in the conversationhistory DB 511, the conversation frame generation unit 502 extractsconversation data exchanged immediately before conversation data due towhich the degree of happiness has increased. A predetermined thresholdvalue, for example, may be used to determine the increase in the degreeof happiness. For example, if the degree of happiness [0, 0, 0, 0] ofthe conversation ID: C01-01 and the degree of happiness [0, 1, 0, 0] ofthe conversation ID: C01-03 of the utterer ID: U01 are compared in anexemplary conversation (conversation ID: C01, for example) in theconversation data illustrated in FIG. 19, it is possible to recognizethat the degree of happiness has increased by “1”. In this case, theconversation frame generation unit 502 extracts the conversation dataexchanged immediately before the conversation data due to which thedegree of happiness has increased, that is, the conversation data(utterer ID: U01) of the conversation ID: C01-01 and the conversationdata (utterer ID: U02) of the conversation ID: C01-02.

Note that in a case in which a feeling value (a value indicating afeeling) calculated on the basis of the characteristic keywordsindicating feelings (hereinafter, abbreviated as “feeling words”)included in the conversation data is applied to each conversation dataitem, a conversation due to which not only the degree of happiness butalso the feeling value have increased may be extracted. Such a feelingvalue can be calculated when the happiness calculation unit 501calculates the degree of happiness. The happiness calculation unit 501has a feeling value table indicating feeling values of each feelingword, retrieves feeling words included in conversation data from aresult of morpheme analysis of the conversation data, and in a case inwhich feeling words are included, the happiness calculation unit 501calculates the feeling value of the conversation data by adding thefeeling values of the felling words. Note that examples of the feelingwords include positive/negative modifying words and interjections. Theextraction of conversation data that is a factor of an increase in adegree of happiness has been described above.

Next, the conversation frame generation unit 502 performs syntaxanalysis on the extracted conversation data (Step S346). Specifically,the conversation frame generation unit 502 divides a character string ofthe extracted conversation data into morphemes and analyzes grammaticalrelationships (a subject—a verb, a modifying word—a modified word, andthe like) between words.

Then, the conversation frame generation unit 502 abstracts a noun phrasein extracted conversation data on the basis of the result of the syntaxanalysis (Step S349) and generates a conversation frame including a verband the abstracted noun phrase (Step S352). Note that in a case in whichthe noun phrase includes an adjective, a value in accordance with afeeling value of the corresponding adjective (a feeling value 1:“positive”, a feeling value −1: “negative”, and a feeling value 0:“neutral”) may be included in the conversation frame with reference tothe feeling value table of adjectives as illustrated in FIG. 21.Hereinafter, specific examples of generation of a conversation framewill be described.

In a case in which extracted conversation data includes “I made a tastygratin yesterday!” (conversation ID: C01-01) and “You can make a tastygratin. That sounds great.” (conversation ID: C01-02), for example, theconversation frame generation unit 502 performs syntax analysis on eachconversation data item, abstracts the common noun phrase (“tasty gratin(an adjective+a noun)” here), and generates a conversation frame asfollows.

Condition frame: “I made a <adjective: positive> <noun>”

Response frame: “You can make a <adjective: positive> <noun>. Thatsounds great!”

The conversation frame includes a “condition (condition frame)” and a“response (response frame)” in which the conversation frame is used asdescribed above. In addition, a value in accordance with a feeling valueof the adjective (the value “positive” corresponding to the feelingvalue “1” of “tasty” here) is also included in the conversation frame.In this manner, it is possible to recognize that the conversation frameis used as a response to an utterance indicating that the utterer hasmade a positive object.

Then, the conversation frame generation unit 502 registers the generatedconversation frame in the conversation frame DB 512 (Step S355). Here,an example of the conversation frame registered in the conversationframe DB 512 is illustrated in FIG. 22. As illustrated in FIG. 22, aframe ID has been applied to each conversation frame.

Then, Steps S343 to S355 described above are repeated until aninstruction for ending them is provided (Step S358). Steps S343 to S355described above are periodically (such as once a day or once in sixhours) performed, for example.

<4-2. Generation of Response Sentence>

Next, generation of a response sentence by using a conversation frameaccording to the first embodiment will be described. The conversation DBgeneration unit 50A according to the embodiment generates conversationframes as described above, and if the number of conversation framesreaches a predetermined number (100 sets, for example), the conversationDB generation unit 50A outputs the conversation frames as a data set tothe dialogue processing unit 30. The dialogue processing unit 30 usesthe conversation frames generated in advance when a response (answervoice) of the agent is generated in response to the utterance (questionvoice) of the user input via the voice agent I/F 20. Here, a specificconfiguration and operation processing of the dialogue processing unit300A that generates a response by using such conversation frames will bedescribed. Note that the configuration of the dialogue processing unit300A is common to those of a basic dialogue processing unit 31, acharacter A dialogue processing unit 32, a person B dialogue processingunit 33, and a person C dialogue processing unit 34.

(4-2-1. Configuration of Dialogue Processing Unit 300A)

FIG. 23 is a diagram of a configuration example of the dialogueprocessing unit 300A according to the first embodiment. As illustratedin FIG. 23, the dialogue processing unit 300A has a question sentenceretrieval unit 310, an answer sentence generation unit 320, aconversation DB 330, a phoneme data acquisition unit 340, a conversationanalysis unit 350, a conversation history DB 360, a response sentencegeneration unit 370, and a conversation frame DB 380.

Since the question sentence retrieval unit 310, the answer sentencegeneration unit 320, the conversation DB 330, and the phoneme dataacquisition unit 340 are similar to configurations with the samereference numerals described above with reference to FIG. 4, descriptionthereof will be omitted here.

The conversation analysis unit 350 acquires a conversation sentence ofthe user that has been acquired by the voice agent I/F 20 and has beenchanged into texts and performs syntax analysis. Note that theconversation analysis unit 350 may accumulate the conversation sentencein the conversation history DB 360. The conversation sentenceaccumulated in the conversation history DB 360 is collected by theconversation DB generation unit 50A.

The response sentence generation unit 370 retrieves a conversation framewith coincident syntax from the conversation frame DB 380 on the basisof the result of analysis performed by the conversation analysis unit350. A data set of the conversation frames accumulated in theconversation frame DB 512 of the conversation DB generation unit 50A issaved in the conversation frame DB 380. The conversation frames saved inthe conversation frame DB 380 may be periodically updated by theconversation DB generation unit 50A.

In a case in which a conversation sentence of the user is “I did it! Imade beautiful flower arrangement!”, for example, the response sentencegeneration unit 370 retrieves a condition (utterance condition frame)that coincides with a result of syntax analysis “I did it! I made a<adjective: positive> <noun>!” for abstracting a noun phrase from theconversation frame DB 380. In a case in which conversation frames asillustrated in FIG. 22 are saved, for example, the condition frame ofthe frame ID: F01 coincides with this condition. Therefore, the responsesentence generation unit 370 generates “You can make beautiful flowarrangement. That sounds great!” as response sentence data by using aresponse frame “You can make a <adjective: positive> <noun>. That soundsgreat!” corresponding to the condition frame. Note that in a case inwhich a plurality of condition frames are retrieved, a condition framethat is retrieved first may be selected, or a condition frame may berandomly selected from among all the retrieved condition frames.

The response sentence data generated by the response sentence generationunit 370 is output to the phoneme data acquisition unit 340, andphonemes of a specific agent are acquired by the phoneme dataacquisition unit 340, and the response sentence data and the phonemedata are output to the voice agent I/F 20. Then, the response sentencedata is vocalized as voice of the specific agent by the voice agent I/F20 and is output as a speech of the specific agent from the clientterminal 1.

The configuration of the dialogue processing unit 300A according to thefirst embodiment has been described above. Next, operation processing ofthe dialogue processing unit 300A according to the embodiment will bedescribed.

(4-2-2. Response Processing)

FIG. 24 is a flowchart illustrating response processing according to thefirst embodiment. As illustrated in FIG. 24, the conversation analysisunit 350 first acquires a conversation sentence of the user that hasbeen acquired by the voice agent I/F 20 and has been changed into texts(Step S363).

Then, the conversation analysis unit 350 performs syntax analysis on theconversation sentence (Step S366).

Then, the conversation analysis unit 350 saves the conversation sentenceas a conversation history in the conversation history DB 360 (StepS369).

Then, the response sentence generation unit 370 generates responsesentence data on the basis of the result of the analysis performed bythe conversation analysis unit 350 and with reference to theconversation frame DB 380 (Step S372). Details of the response sentencegeneration processing will be described later.

Then, the response sentence generation unit 370 outputs the generatedresponse sentence data to the phoneme data acquisition unit 340 (StepS375).

(4-2-3. Response Sentence Generation Processing)

Then, the response sentence generation processing in Step S372 describedabove will be described with reference to FIG. 25. FIG. 25 is aflowchart illustrating response sentence generation processing accordingto the first embodiment.

As illustrated in FIG. 25, the response sentence generation unit 370first matches the result of analyzing the conversation sentence by theconversation analysis unit 350 and the conversation frames saved in theconversation frame DB 380 (Step S383).

Then, the response sentence generation unit 370 selects a conversationframe that includes a condition frame that matches the result ofanalyzing the conversation sentence (Step S386).

Then, the response sentence generation unit 370 generates responsesentence data by using the response frame of the selected conversationframe (Step S389).

(4-2-4. Response Sentence Output Processing)

Next, processing of outputting response sentence data generated by theresponse sentence generation unit 370 will be described with referenceto FIG. 26. FIG. 26 is a flowchart illustrating response sentence dataoutput processing according to the first embodiment.

As illustrated in FIG. 26, the phoneme data acquisition unit 340 firstperforms syntax analysis on response sentence data generated by theresponse sentence generation unit 370 (Step S393) and acquires phonemedata of a specific agent corresponding to the respective elements (StepS396).

Next, the phoneme data acquisition unit 340 outputs the acquired phonemedata and the response sentence data to the voice agent I/F 20 (StepS399).

Then, the voice agent I/F 20 generates a voice spectrum from the phonemedata and vocalizes the response sentence data (voice synthesis) (StepS402).

Then, the voice agent I/F 20 transmits the generated response voice tothe client terminal 1, and the response voice is output by the clientterminal 1 (Step S405).

5. Dialogue Control Processing According to Second Embodiment <5-1.Generation of Conversation Frame>

In the aforementioned first embodiment, the method of extracting adegree of happiness (how happy the utterer feels) of the conversationdata from the conversation DB 511 in generation of the conversationframe and learning the conversation frame when the degree of happinesshas increased has been described.

Here, persons have respective attributes, and there are cases in whichconversations that make the persons happy differ. Thus, the secondembodiment makes it possible to generate a response to a user by using aconversation frame in accordance with an attribute of the user andprovide a more effective response by analyzing the attribute of theperson who has made the conversation and learning the conversation framefor each attribute.

First, definitions of attributes in the embodiment will be described.Two ideas, namely typology and a characteristic theory are used toexplain attributes of persons. Attributes are explained by some typicaltypes according to the typology while attributes are explained ascombinations of some characteristics according to the characteristictheory. In addition, the personalities that are attributes in a broadsense may be classified into a congenital temperament and an acquiredtemperament. In the embodiment, a personality theory that is acharacteristic theory suggested by Robert Cloninger is used as anexample. According to such a personality theory, attributes areclassified on the basis of a total of seven characteristics, namely fourtemperament parameters “novelty seeking, reward dependence, harmavoidance, and persistence” and three attribute parameters“self-directedness, cooperativeness, and self-transcendence”(seven-dimensional personality model). For relevance between therespective features of the temperament parameters and neurotransmitters,there has been a study which demonstrates that the novelty seeking isrelevant to dopamine that is a neurotransmitter, reward dependence isrelevant to norepinephrine, and harm avoidance is relevant to serotonin.In the embodiment, a three-dimensional space along axes of threetemperament parameters, namely novelty seeking, reward dependence, andharm avoidance is considered, and attributes are classified into eightattributes (adventurer, an explosive attribute, a passionate attribute,a nervous attribute, an independent attribute, a logical attribute, aserious attribute, and a careful attribute) depending on how large thevalues of the respective axes are, as illustrated in FIG. 27.

(5-1-1. Configuration of Conversation DB Generation Unit 50A)

Next, a main configuration of a conversation DB generation unit 50B thatgenerates a conversation frame will be described with reference to FIG.28. FIG. 28 is a diagram illustrating a configuration example of theconversation DB generation unit 50B according to the second embodiment.As illustrated in FIG. 28, the conversation DB generation unit 50B has acontrol unit 520, a communication unit 510, a conversation history DB511, a conversation frame DB 512, and an attribute DB 513.

Since the communication unit 510, the conversation history DB 511, andthe conversation frame DB 512 are similar to the configurations with thesame reference numerals in the first embodiment described with referenceto FIG. 15, description thereof will be omitted here.

The control unit 520 functions as an arithmetic processing device and acontrol device and controls overall operations in the conversation DBgeneration unit 50B in accordance with a variety of programs. Thecontrol unit 520 is realized by an electronic circuit such as a CPU or amicroprocessor, for example. In addition, the control unit 520 accordingto the embodiment functions as the happiness calculation unit 501, theconversation frame generation unit 502, and the attribute analysis unit503.

Functions of the happiness calculation unit 501 and the conversationframe generation unit 502 are similar to those of the configurationswith the same reference numerals in the first embodiment described withreference to FIG. 15.

The attribute analysis unit 503 refers to the conversation dataaccumulated in the conversation history DB 511 and calculates anattribute parameter of the utterer of each conversation data item. Thecalculated attribute parameter is linked with the conversation data andis accumulated in the conversation history DB 511. In addition, theattribute analysis unit 503 extracts the attribute parameter of theconversation data of each utterer ID from the conversation history DB511, analyzes an attribute type of each utterer ID on the basis of theattribute parameter, and accumulates the attribute type in the attributeDB 513. Details of the attribute analysis will be described later.

Information related to the attribute type of each utterer, which hasbeen analyzed by the attribute analysis unit 503, is accumulated in theattribute DB 513

The specific configuration of the conversation DB generation unit 50Baccording to the second embodiment has been described above. Next,operation processing of the conversation DB generation unit 50Baccording to the second embodiment will be described.

(5-1-2. Conversation Frame Generation Processing)

FIG. 29 is a flowchart illustrating conversation frame generationprocessing according to the second embodiment. As illustrated in FIG.16, the conversation DB generation unit 50A first acquires conversationdata between users from social media on the network, for example, viathe communication unit 510 (Step S413). The acquired conversation datais accumulated in the conversation history DB 511.

Then, the attribute analysis unit 503 calculates an attribute parameterof the utterer of each conversation on the basis of the conversationdata accumulated in the conversation history DB 511 (Step S416). Thecalculated attribute parameter is linked to the conversation data and issaved in the conversation history DB 511. Calculation of the attributeparameter will be described in detail with reference to FIG. 30.

Next, the happiness calculation unit 501 calculates a degree ofhappiness of the utterer of each conversation on the basis of theconversation data accumulated in the conversation history DB 511 (StepS419). The calculated degree of happiness is linked to the conversationdata and is saved in the conversation history DB 511. Calculation of thedegree of happiness is as described above with reference to FIG. 17.

Then, the conversation frame generation unit 502 refers to the degree ofhappiness of each conversation and generates a conversation frame thatgives the user a happy feeling (Step S422). The generated conversationframe is accumulated in the conversation frame DB 512. Generation of theconversation frame is as described above with reference to FIG. 20. Notethat am attribute type for which use of the conversation frame isconsidered to be appropriate is linked as metadata in the secondembodiment. Specifically, the conversation frame generation unit 502acquires the attribute type of the utterer ID, the degree of happinessof which has increased, in the conversation data used when theconversation frame is generated, from the attribute DB 513 and links theattribute type as metadata to the generated conversation frame.

Then, Steps S413 to S422 described above are repeated until aninstruction for ending them is provided (Step S425). Steps S413 to S422described above are periodically (such as once a day or once in sixhours) performed, for example.

(5-1-3. Attribute Analysis Processing)

Next, attribute analysis processing performed in Step S416 describedabove will be described in detail. FIG. 30 is a flowchart illustratingthe attribute analysis processing according to the second embodiment.

As illustrated in FIG. 30, the attribute analysis unit 503 firstperforms language analysis on the conversation data accumulated in theconversation history DB 511 (Step S433). For example, the attributeanalysis unit 503 performs morpheme analysis on the conversation data(texts).

Then, the attribute analysis unit 503 performs expression retrieval onthe conversation data on the basis of the result of the analysis (StepS436) and calculates an attribute parameter (Step S439). Here, athree-dimensional space along three axes, novelty seeking, rewarddependence, harm avoidance is considered in the embodiment as describedabove with reference to FIG. 27, and attributes are classified intoeight attributes (an adventurer, an explosive attribute, a passionateattribute, a nervous attribute, an independent attribute, a logicalattribute, a serious attribute, and a careful attribute) depending inhow large the values of the respective axes are. The attribute analysisunit 503 calculates an attribute parameter on the basis of a frequencyat which characteristic keywords corresponding to these three axes areincluded in the conversation data. Here, an example of three attributeparameter contribution values in characteristic keywords is illustratedin FIG. 31. The attribute analysis unit 503, for example, has the tableillustrated in FIG. 31.

The three attribute parameter contribution values (novelty seeking,reward dependence, and presence avoidance) illustrated in FIG. 31 areexpressed as 0 to 1. In a case in which the keyword (that is, aconversation expression) “feel comfortable with stimulation” on thefirst line is included in conversation data, for example, the value ofnovelty seeking is “1” while the other values are “0”. In addition, in acase in which the keyword “become a habit” on the fourth line isincluded in conversation data, the value of reward dependence is “1”while the other values are “0”.

Therefore, the attribute analysis unit 503 retrieves all expressions(keywords) included in the table illustrated in FIG. 31 from theconversation data on the basis of the result of the morpheme analysis(word extraction) performed on the conversation data, and acquires avector of an attribute parameter obtained by adding each attributeparameter contribution values for the discovered expression. If it isassumed that the respective attribute parameter contribution values areg_(1 to 3), a vector of an attribute parameter is obtained by thefollowing Equation 2.

[Math. 2]

C=[Σ_(i) g _(1i),Σ_(i) g _(2i),Σ_(i) g _(3i)]   Equation 2

The expression “I am worry about if I can make it in time” in the tableof the attribute parameter contribute values is included in conversationdata “I did study only for two hours today. I am worry about if I canmake it in time before the examination.” (utterer ID: U03), and theattribute parameter contribution value becomes c=[0.0, 0.0, 1.0] (thevalue of harm avoidance is “1.0”) referring to the table illustrated inFIG. 31.

Then, the attribute analysis unit 503 saves the calculated attributeparameter as metadata of each conversation data item in the conversationhistory DB 511 (Step S442). Here, an example of conversation dataaccumulated in the conversation history DB 511 is illustrated in FIG.32. The conversation data is data to which conversation IDs, dates andtimes of conversations, IDs of persons who have made conversations,texts (content of conversation), degrees of happiness, and attributeparameters are linked.

Next, the attribute analysis unit 503 updates the attribute DB 513 (StepS445). Information related to the attribute type of each utterer isaccumulated in the attribute DB 513. The attribute type of the utterercan be determined on the basis of eight classifications in thethree-dimensional space described with reference to FIG. 27. Here, anexample of attribute data of the utterer accumulated in the attribute DB513 is illustrated in FIG. 33. As illustrated in FIG. 33, utterer IDs,the numbers of utterances, attribute parameters, and attribute types arelinked to the attribute data in the attribute DB 513. The attributeanalysis unit 503 adds “1” to the number of utterances of the uttererID: U03 of the aforementioned conversation data (“I did study only fortwo hours today. I am worry about if I can make it in time before theexamination.”), for example, and adds “1.0” to “harm avoidance” as anattribute parameter of the utterer.

The attribute type of the utterer can be determined on the basis of theeight classifications in the three-dimensional space described withreference to FIG. 27. Specifically, the attribute analysis unit 503calculates the three attribute parameters (novelty seeking, rewarddependence, and harm avoidance) per one utterance by dividing the value(sum) of each attribute parameter by the number of utterances (totalnumber) and determines which of the eight classifications illustrated inFIG. 27 the attribute type corresponds to. Note that the attributeanalysis unit 503 may classify the value of each attribute parameter atthe threshold value of 0.5 and determine the attribute type. Here, anexample of the three-dimensional space of the attribute parametercontribution values and the attribute types is illustrated in FIG. 34.

In the example illustrated in FIG. 34, eight attribute types in a casein which attribute parameters along three axes are classified at athreshold value of 0.5 are represented in the three-dimensional space.In this manner, it is possible to recognize that features of therespective attribute types are as follows.

-   -   Attribute type 000: An “independent” type who exhibits low        novelty seeking, reward dependence, and harm avoidance    -   Attribute type 001: A “logical attribute” type who exhibits low        novelty seeking and reward dependence but has high harm        avoidance    -   Attribute type 011: A “careful attribute” type who exhibits low        novelty seeking but has high reward dependence and harm        avoidance    -   Attribute type 111: A “nervous attribute” type who exhibits high        novelty seeking, reward dependence, and harm avoidance    -   Attribute type 010: A “serious attribute” type who exhibits low        novelty seeking and harm avoidance but high reward dependence    -   Attribute type 110: A “passionate attribute” type who exhibits        high novelty seeking and reward dependence but has low harm        avoidance    -   Attribute type 100: An “adventurer” type who exhibits high        novelty seeking but has low reward dependence and harm avoidance    -   Attribute type 101: An “explosive attribute” type who exhibits        high novelty seeking and harm avoidance but has low reward        dependence

When the attribute type of the utterer ID: U01 illustrated on the firstline in FIG. 33 is determined, for example, the attribute analysis unit503 first calculates attribute parameters per utterance as follows.

Novelty seeking: 127.9/2736=0.046

Reward dependence: 354.2/2736=0.13

Harm avoidance: 2012.4/2736=0.73

In this manner, the attribute analysis unit 503 can classify thecalculated attribute parameters at the threshold value of 0.5 anddetermine that the attribute type is “001” of low novelty seeking, lowreward dependence, and high harm avoidance.

The attribute type determined as described above is linked as metadatato the conversation frame generated in Step S422 (see FIG. 29) describedabove. That is, the conversation learning unit 502 acquires, from theattribute DB 513, the attribute type corresponding to the utterance ID,due to the utterance of which the degree of happiness has increased, inthe conversation data as a basis of the generated conversation frame andlinks the attribute type to the generated conversation frame. Forexample, an exemplary case in which a conversation frame is generated onthe basis of a series of conversations (conversation ID: C01) of “I madea tasty gratin yesterday!” (utterance ID: U01), “You can make a tastygratin. That sounds great.” (utterance ID: U02), and “Not really great,but thank you” (utterance ID: U01) will be described. In this case, theattribute type 001 corresponding to the utterer ID: U01 who has made theutterance of “Not really great, but thank you” due to which the degreeof happiness has increased is linked to the conversation frame. FIG. 35illustrates an example of conversation frames registered in theconversation frame DB 512. As illustrated in FIG. 25, the attributetypes are linked to the respective conversation frames. In this manner,it is possible to select a conversation frame to be used in accordancewith the attribute type of the user and to more effectively lead theuser's feeling to a predetermined feeling (a happy feeling here) in theembodiment.

Then, Steps S433 to S445 described above are repeated until aninstruction for ending them is provided (Step S448). Steps S433 to S445described above are periodically (such as once a day or once in sixhours) performed, for example.

<5-2. Generation of Response Sentence>

(5-2-1. Configuration of Dialogue Processing Unit 300B)

Next, a configuration of a dialogue processing unit 300B according tothe second embodiment will be described. FIG. 36 is a diagramillustrating a configuration example of the dialogue processing unit300B according to the second embodiment. As illustrated in FIG. 36, thedialogue processing unit 300B has a question sentence retrieval unit310, an answer sentence generation unit 320, a conversation DB 330, aphoneme data acquisition unit 340, a conversation analysis unit 350, aconversation history DB 360, a response sentence generation unit 370, aconversation frame DB 380, and an attribute type DB 390.

Since the question sentence retrieval unit 310, the answer sentencegeneration unit 320, the conversation DB 330, the phoneme dataacquisition unit 340, a conversation analysis unit 350, and theconversation history DB 360 are similar to those in the first embodimentas illustrated in FIG. 23, description thereof will be omitted here.

The response sentence generation unit 370 retrieves a conversation frameincluding coincident syntax from the conversation frame DB 380 on thebasis of the analysis result of the conversation analysis unit 350. Adata set of conversation frames accumulated in the conversation frame DB512 of the conversation DB generation unit 50B is saved in theconversation frame DB 380. Note that attribute types for which theconversation frames are used are described in addition to the contentdescribed in the first embodiment in the conversation frame DB 380 (seeFIG. 35). The conversation frames are used only for persons of theattribute types that are the same as the attribute types in theembodiment.

In a case in which a conversation sentence of user is “I did it! I madebeautiful flower arrangement!”, for example, the response sentencegeneration unit 370 retrieves a condition (utterance condition frame)that coincides with a result of syntax analysis for abstracting a nounphrase, namely “I did it! I made a <adjective: positive> <noun>!” fromthe conversation frame DB 380. At this time, an attribute type of theuser is acquired from the attribute type DB 390 and is used for theretrieval. In a case in which conversation frames as illustrated in FIG.35 are saved, for example, condition frames of the frame IDs: F01, F04,and F05 coincide with each other.

In a case in which the attribute type of the user is a type of strongharm avoidance: 001, the response sentence generation unit 370 selectsthe conversation frame F01 and generates the following responsesentence: “You can make beautiful flower arrangement! That soundsgreat!”

In contrast, in a case in which the attribute type of the user is a typeof strong reward dependence: 010, the response sentence generation unit370 selects the conversation frame F04 and generates the followingresponse sentence: “Making beautiful flower arrangement is fun! Let'smake more!”

In addition, in a case in which the attribute type of the user is a typeof strong novelty seeking: 100, the response sentence generation unit370 selects the conversation frame F05 and generates the followingresponse sentence: “You can make more beautiful flower arrangement! Youcan do it!”

The response sentence data generated by the response sentence generationunit 370 is output to the phoneme data acquisition unit 340, phonemes ofa specific agent are acquired by the phoneme data acquisition unit 340,and the response sentence data and the phoneme data are output to thevoice agent I/F 20. Then, the response sentence data is vocalized asvoice of the specific agent by the voice agent I/F 20 and is then outputas a speech of the specific agent from the client terminal 1.

The configuration of the dialogue processing unit 300B according to thefirst embodiment has been described above. Next, operation processing ofthe dialogue processing unit 300B according to the embodiment will bedescribed.

(5-2-2. Response Sentence Generation Processing)

FIG. 37 is a flowchart illustrating response sentence generationprocessing according to the second embodiment. As illustrated in FIG.37, the response sentence generation unit 370 first acquires an ID of aperson who has made a conversation (Step S453). The ID of the person whohas made a conversation can be transmitted from the client terminal 1 ofthe user, for example.

Next, the response sentence generation unit 370 acquires an attributetype of the ID of the person who has made the conversation from theattribute type DB 390 (Step S456).

Then, the response sentence generation unit 370 matches a result ofanalyzing a conversation sentence (utterance voice of the user) by theconversation analysis unit 350 with conversation frames accumulated inthe conversation frame DB 380 (Step S459). The conversation analysisunit 350 performs syntax analysis on the conversation sentence of theuser that has been acquired by the voice agent I/F 20 and has beenchanged into texts, in a manner similar to that in the first embodiment.

Next, the response sentence generation unit 370 further selects aconversation frame that matches the attribute type of the person who hasmade the conversation (user) from among the conversation framesincluding condition frames that match the analysis result (Step S462).

Then, the response sentence generation unit 370 generates responsesentence data by using a response frame of the selected conversationframe (Step S465).

The response sentence generation processing according to the embodimenthas been described above. Note that since the response sentence outputprocessing according to the second embodiment is similar to the responsesentence output processing according to the first embodiment asdescribed above with reference to FIG. 26, description thereof will beomitted here.

6. Conclusion

As described above, the communication control system according to theembodiment of the present disclosure can lead the user to apredetermined feeling by using a conversation structure generated fromactual conversations between users.

The preferred embodiment(s) of the present disclosure has/have beendescribed above with reference to the accompanying drawings, whilst thepresent disclosure is not limited to the above examples. A personskilled in the art may find various alterations and modifications withinthe scope of the appended claims, and it should be understood that theywill naturally come under the technical scope of the present disclosure.

For example, it is possible to also generate a computer program causinghardware such as the CPU, the ROM, and the RAM contained in the clientterminal 1 or the agent server 2 described above to realize the functionof the client terminal 1 or the agent server 2. In addition, acomputer-readable storage medium that stores the computer program isalso provided.

In addition, in the above-described embodiment, the configuration inwhich various functions are realized by the agent server 2 on theInternet has been described, but the embodiment is not limited thereto.At least a part of the configuration of the agent server 2 illustratedin FIG. 3 may be realized in the client terminal 1 (a smartphone, awearable terminal, or the like) of the user. In addition, the wholeconfiguration of the agent server 2 illustrated in FIG. 3 may beinstalled in the client terminal 1 so that the client terminal 1 canperform all the processes.

In addition, although the aforementioned embodiment in which theresponse sentence data is vocalized by the voice agent I/F 20 by usingpredetermined phoneme data, and the voice is transmitted to the clientterminal 1 and outputs as a speech from the agent has been described,the embodiment is not limited thereto. For example, the voice agent I/F20 may transmit the response sentence data and the phoneme data to theclient terminal 1, and the client terminal 1 may vocalize the responsesentence data by using the phoneme data and output the voice as a speechof the agent,

Further, the effects described in this specification are merelyillustrative or exemplified effects, and are not limitative. That is,with or in the place of the above effects, the technology according tothe present disclosure may achieve other effects that are clear to thoseskilled in the art from the description of this specification.

Additionally, the present technology may also be configured as below.

-   -   (1)

A communication system including:

a communication unit that receives a conversation of a user;

an accumulation unit that accumulates a conversation frame thatdescribes a structure of a conversation generated on a basis of theconversation of the user collected via the communication unit; and

a control unit that obtains a feeling parameter related to a feeling ofthe user who sends the conversation in units of the collectedconversation, extracts the conversation frame from the conversation on abasis of the feeling parameter, and accumulates the conversation framein the accumulation unit.

-   -   (2)

The communication system according to (1),

in which the control unit

-   -   analyzes the structure of the conversation of the user received        from the client terminal via the communication unit, and    -   generates response text of an agent on a basis of the        conversation frame that coincides with the analyzed structure        and that is accumulated in the accumulation unit.    -   (3)

The communication system according to (2),

in which the control unit

-   -   associates the conversation frame with attribute information of        the user who has the conversation which is a basis of the        conversation frame, and accumulates the attribute information        and the conversation frame in the accumulation unit, and    -   extracts, from the accumulation unit, the conversation frame        that corresponds to the attribute information of the user of the        client terminal and a structure that coincides with the        structure of the analyzed conversation of the user, and        generates response text on a basis of the extracted conversation        frame.    -   (4)

The communication system according to (2) or (3), in which the controlunit transmits the generated response text to the client terminal viathe communication unit.

-   -   (5)

The communication system according to (2) or (3), in which the controlunit generates voice synthesis data by performing voice synthesis on theresponse text and transmits the voice synthesis data to the clientterminal via the communication unit.

-   -   (6)

The communication system according to any one of (1) to (5), in whichthe control unit links a conversation response frame that describes astructure of a conversation of a second user, which is estimated to be afactor in an increase in a level of the feeling parameter correspondingto a conversation of a first user, to a conversation condition framethat describes a structure of the conversation of the first user thatoccurs chronologically before the conversation of the second user, whichis estimated to be the factor, and accumulates the conversation responseframe and the conversation condition frame in the accumulation unit.

-   -   (7)

The communication system according to (6),

in which the control unit analyzes a structure of a conversation of auser received from a client terminal via the communication unit, and

if the conversation condition frame that coincides with the analyzedstructure is detected from the accumulation unit, the control unitspecifies a conversation response frame accumulated in association withthe detected conversation condition frame, and generates a response textof an agent on a basis of the conversation response frame.

-   -   (8)

The communication system according to (7),

in which the control unit associates attribute information of the firstuser with the conversation condition frame and the conversation responseframe and accumulates the attribute information, the conversationcondition frame, and the conversation response frame in the accumulationunit, and

if the conversation condition frame that corresponds to the analyzedstructure and the attribute information of the user is detected from theaccumulation unit, the control unit specifies a conversation responseframe accumulated in association with the detected conversationcondition frame, and generates response text of an agent on a basis ofthe conversation response frame.

-   -   (9)

The communication system according to any one of (6) to (8), in whichthe control unit links a conversation response frame that describes astructure of a conversation of the second user, which is estimated to bea factor in an increase in a degree of happiness that represents a levelof the feeling parameter corresponding to a conversation of the firstuser, to a conversation condition frame that describes a structure of aconversation of the first user that occurs chronologically before theconversation of the second user, which is estimated to be the factor,and accumulates the conversation response frame and the conversationcondition frame in the accumulation unit.

-   -   (10)

The communication system according to (9), in which the degree ofhappiness is calculated on a basis of four factors related to happiness.

-   -   (11)

The communication system according to any one of (1) to (10), in whichthe control unit collects a conversation of the user on social media viathe communication unit.

-   -   (12)

The communication system according to (11), in which the control unitcollects a voice conversation of the user via the communication unit.

-   -   (13)

A communication control method including, by a processor:

receiving a conversation of a user by a communication unit;

accumulating, in an accumulation unit, a conversation frame thatdescribes a structure of a conversation generated on a basis of theconversation of the user collected via the communication unit;

obtaining a feeling parameter related to a feeling of the user who sendsthe conversation in units of the collected conversation; and

extracting the conversation frame from the conversation on a basis ofthe feeling parameter and accumulating the conversation frame in theaccumulation unit.

REFERENCE SIGNS LIST

-   1 client terminal-   2 agent server-   30 dialogue processing unit-   300, 300A, 300B dialogue processing unit-   310 question sentence retrieval unit-   320 answer sentence generation unit-   330 conversation DB-   340 phoneme data acquisition unit-   350 conversation analysis unit-   360 conversation history DB-   370 response sentence generation unit-   380 conversation frame DB-   390 attribute type DB-   31 basic dialogue processing unit-   32 character A dialogue processing unit-   33 person B dialogue processing unit-   34 person C dialogue processing unit-   40 phoneme storage unit-   41 basic phoneme DB-   42 character A phoneme DB-   43 person B phoneme DB-   44 person C phoneme DB-   50, 50A, 50B conversation DB generation unit-   500, 520 control unit-   501 happiness analysis unit-   502 conversation learning unit-   503 attribute analysis unit-   510 communication unit-   511 conversation history DB-   512 conversation frame DB-   513 attribute DB-   60 phoneme DB generation unit-   70 advertisement insertion processing unit-   72 advertisement DB-   80 feedback acquisition processing unit-   3 network

1. A communication system, comprising: circuitry configured to: receivea conversation of a first user; obtain a feeling parameter of the firstuser based on the conversation of the first user; obtain a conversationresponse frame and a conversation condition frame based on an increasein a level of the feeling parameter of the first user, wherein theconversation response frame describes a structure of a conversation of asecond user in response to the conversation of the first user, and theconversation condition frame describes a structure of the conversationof the first user; accumulate the conversation response frame inassociation with the conversation condition frame in an accumulationunit; detect the conversation condition frame corresponding to astructure of a conversation of a third user from the accumulation unit;specify the conversation response frame accumulated in association withthe detected conversation condition frame; and generate a response of anagent based on the specified conversation response frame.
 2. Thecommunication system according to claim 1, wherein the circuitry isfurther configured to transmit the generated response to a clientterminal.
 3. The communication system according to claim 1, wherein thecircuitry is further configured to receive the conversation of the thirduser from a client terminal.
 4. The communication system according toclaim 1, wherein the circuitry is further configured to extract firstconversation data from the conversation of the first user based on eachof second conversation data and the feeling parameter, a degree ofhappiness of the second conversation data is greater than a degree ofhappiness of the first conversation data, and the second conversationdata is subsequent to the first conversation data in the conversation ofthe first user.
 5. The communication system according to claim 4,wherein the circuitry is further configured to calculate an attributeparameter of the first user based on the first conversation data, andthe attribute parameter of the first user corresponds to temperament ofthe first user.
 6. The communication system according to claim 5,wherein the circuitry is further configured to generate a conversationframe for the agent based on the first conversation data and theattribute parameter of the first user, and the conversation frameincludes the conversation response frame and the conversation conditionframe.
 7. The communication system according to claim 5, wherein thecircuitry is further configured to accumulate the attribute parameter inassociation with the first conversation data in the accumulation unit.8. The communication system according to claim 1, wherein the circuitryis further configured to: generate voice synthesis data based on voicesynthesis on the generated response; and transmit the generated voicesynthesis data to a client terminal.
 9. The communication systemaccording to claim 1, wherein the circuitry is further configured tocollect the conversation of the first user from messages exchanged onsocial media.
 10. A communication method, comprising: receiving aconversation of a first user; obtaining a feeling parameter of the firstuser based on the conversation of the first user; obtaining aconversation response frame and a conversation condition frame based onan increase in a level of the feeling parameter of the first user,wherein the conversation response frame describes a structure of aconversation of a second user in response to the conversation of thefirst user, and the conversation condition frame describes a structureof the conversation of the first user; accumulating the conversationresponse frame in association with the conversation condition frame inan accumulation unit; detecting the conversation condition framecorresponding to a structure of a conversation of a third user from theaccumulation unit; specifying the conversation response frameaccumulated in association with the detected conversation conditionframe; and generating a response of an agent based on the specifiedconversation response frame.
 11. The communication method according toclaim 10, further comprising extracting first conversation data from theconversation of the first user based on each of second conversation dataand the feeling parameter, wherein a degree of happiness of the secondconversation data is greater than a degree of happiness of the firstconversation data, and the second conversation data is subsequent to thefirst conversation data in the conversation of the first user.
 12. Thecommunication method according to claim 11, further comprisingcalculating an attribute parameter of the first user based on the firstconversation data, wherein the attribute parameter of the first usercorresponds to temperament of the first user.
 13. The communicationmethod according to claim 12, further comprising generating aconversation frame for the agent based on the first conversation dataand the attribute parameter of the first user, wherein the conversationframe includes the conversation response frame and the conversationcondition frame.
 14. The communication method according to claim 12,further comprising accumulating the attribute parameter in associationwith the first conversation data in the accumulation unit.
 15. Anon-transitory computer-readable medium having stored thereon,computer-executable instructions which, when executed by a processor,cause the processor to execute operations, the operations comprising:receiving a conversation of a first user; obtaining a feeling parameterof the first user based on the conversation of the first user; obtaininga conversation response frame and a conversation condition frame basedon an increase in a level of the feeling parameter of the first user,wherein the conversation response frame describes a structure of aconversation of a second user in response to the conversation of thefirst user, and the conversation condition frame describes a structureof the conversation of the first user; accumulating the conversationresponse frame in association with the conversation condition frame inan accumulation unit; detecting the conversation condition framecorresponding to a structure of a conversation of a third user from theaccumulation unit; specifying the conversation response frameaccumulated in association with the detected conversation conditionframe; and generating a response of an agent based on the specifiedconversation response frame.